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Abstract:

Mechanisms for performing a duration-based operation are provided. At
least one document is received having a plurality of associated dates
and/or times and concepts associated with the dates and/or times. The at
least one document does not explicitly specify a duration between the
dates and/or times. Dates and/or times in the at least one document
having similar associated concepts are correlated and, for the correlated
dates and/or times, an implicit duration is calculated based on the dates
and/or times. The concepts are associated with the implicit duration and
a first document in the at least one document is annotated with an
implicit duration annotation that specifies the implicit duration and the
associated concepts. A duration based operation is then performed based
on the implicit duration annotation.

Claims:

1. A method, in a data processing system comprising a processor and a
memory, for performing a duration-based operation, the method comprising:
receiving, by the data processing system, at least one document having a
plurality of associated dates and/or times and concepts associated with
the dates and/or times, wherein the at least one document does not
explicitly specify a duration between the dates and/or times;
correlating, by the data processing system, dates and/or times in the at
least one document having similar associated concepts; calculating, by
the data processing system, for the correlated dates and/or times, an
implicit duration based on the dates and/or times; associating, by the
data processing system, the associated concepts with the implicit
duration; annotating, by the data processing system, a first document in
the at least one document with an implicit duration annotation that
specifies the implicit duration and the associated concepts; and
performing, by the data processing system, the duration-based operation
based on the implicit duration annotation.

2. The method of claim 1, further comprising: analyzing the at least one
document to identify the dates and/or times; and performing natural
language processing to identify textual content corresponding to concepts
associated with the dates and/or times identified in the at least one
document.

3. The method of claim 2, wherein performing natural language processing
to identify textual content corresponding to concepts associated with the
dates and/or times comprises identifying keywords or phrases within a
predetermined textual range of the dates and/or times.

4. The method of claim 1, wherein calculating an implicit duration based
on the dates and/or times comprises, for each portion of a plurality of
portions of the at least one document associated with a date and/or time,
calculating one or more implicit durations based on a comparison of a
date and/or time associated with the portion to a date and/or time of
another portion of the at least one document.

5. The method of claim 1, wherein calculating an implicit duration based
on the dates and/or times comprises: clustering portions of the at least
one document based on similarity of concepts in the portions of the at
least one document; and calculating at least one implicit duration for
each cluster generated by the clustering based on a comparison of dates
and/or times associated with the portions of the at least one document
associated with the cluster.

6. The method of claim 5, wherein the at least one implicit duration for
each cluster is calculated to be a largest difference in date and/or time
between dates and/or times associated with the portions of the at least
one document associated with the cluster.

7. The method of claim 1, wherein the first document is a document
specified through configuration of the data processing system to be a
primary document type, and wherein other documents in the at least one
document that are not designated to be the primary document type are not
annotated.

8. The method of claim 1, wherein the data processing system is a
Question Answering (QA) system, and wherein performing the duration-based
operation based on the implicit duration annotation comprises generating
an answer to an input question based on the implicit duration annotation.

9. The method of claim 8, wherein the duration-based operation further
comprises modifying scoring of one or more candidate answers for the
input question based on a correspondence between the implicit duration
specified in the implicit duration annotation and one or more duration
criteria associated with the one or more candidate answers.

10. The method of claim 1, wherein the at least one document comprises a
patient medical record, and wherein performing the duration-based
operation comprises generating and outputting a treatment recommendation
for a patient corresponding to the patient medical record based on a
comparison of the implicit duration to duration requirements in one or
more medical treatment policies.

11-21. (canceled)

Description:

BACKGROUND

[0001] The present application relates generally to an improved data
processing apparatus and method and more specifically to mechanisms for
performing implicit duration calculations and similarity comparisons in
question answering systems.

[0002] With the increased usage of computing networks, such as the
Internet, humans are currently inundated and overwhelmed with the amount
of information available to them from various structured and unstructured
sources. However, information gaps abound as users try to piece together
what they can find that they believe to be relevant during searches for
information on various subjects. To assist with such searches, recent
research has been directed to generating Question and Answer (QA) systems
which may take an input question, analyze it, and return results
indicative of the most probable answer to the input question. QA systems
provide automated mechanisms for searching through large sets of sources
of content, e.g., electronic documents, and analyze them with regard to
an input question to determine an answer to the question and a confidence
measure as to how accurate an answer is for answering the input question.

[0003] Examples, of QA systems are Siri® from Apple®, Cortana®
from Microsoft®, and the Watson® system available from
International Business Machines (IBM®) Corporation of Armonk, N.Y.
The Watson® system is an application of advanced natural language
processing, information retrieval, knowledge representation and
reasoning, and machine learning technologies to the field of open domain
question answering. The Watson® system is built on IBM's DeepQA®
technology used for hypothesis generation, massive evidence gathering,
analysis, and scoring. DeepQA® takes an input question, analyzes it,
decomposes the question into constituent parts, generates one or more
hypothesis based on the decomposed question and results of a primary
search of answer sources, performs hypothesis and evidence scoring based
on a retrieval of evidence from evidence sources, performs synthesis of
the one or more hypothesis, and based on trained models, performs a final
merging and ranking to output an answer to the input question along with
a confidence measure.

SUMMARY

[0004] In one illustrative embodiment, a method, in a data processing
system comprising a processor and a memory, for performing a
duration-based operation is provided. The method comprises receiving, by
the data processing system, at least one document having a plurality of
associated dates and/or times and concepts associated with the dates
and/or times. The at least one document does not explicitly specify a
duration between the dates and/or times. The method further comprises
correlating, by the data processing system, dates and/or times in the at
least one document having similar associated concepts and calculating, by
the data processing system, for the correlated dates and/or times, an
implicit duration based on the dates and/or times. In addition, the
method comprises associating, by the data processing system, the
associated concepts with the implicit duration and annotating, by the
data processing system, a first document in the at least one document
with an implicit duration annotation that specifies the implicit duration
and the associated concepts. Moreover, the method comprises performing,
by the data processing system, the duration-based operation based on the
implicit duration annotation.

[0005] In other illustrative embodiments, a computer program product
comprising a computer useable or readable medium having a computer
readable program is provided. The computer readable program, when
executed on a computing device, causes the computing device to perform
various ones of, and combinations of, the operations outlined above with
regard to the method illustrative embodiment.

[0006] In yet another illustrative embodiment, a system/apparatus is
provided. The system/apparatus may comprise one or more processors and a
memory coupled to the one or more processors. The memory may comprise
instructions which, when executed by the one or more processors, cause
the one or more processors to perform various ones of, and combinations
of, the operations outlined above with regard to the method illustrative
embodiment.

[0007] These and other features and advantages of the present invention
will be described in, or will become apparent to those of ordinary skill
in the art in view of, the following detailed description of the example
embodiments of the present invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0008] The invention, as well as a preferred mode of use and further
objectives and advantages thereof, will best be understood by reference
to the following detailed description of illustrative embodiments when
read in conjunction with the accompanying drawings, wherein:

[0009] FIG. 1 depicts a schematic diagram of one illustrative embodiment
of a question/answer creation (QA) system in a computer network;

[0010] FIG. 2 is a block diagram of an example data processing system in
which aspects of the illustrative embodiments are implemented;

[0011] FIG. 3 illustrates a QA system pipeline for processing an input
question in accordance with one illustrative embodiment;

[0012] FIG. 4 is an example medical policy and patient clinical history,
as may be provided in an electronic patient medical record for example,
in accordance with one illustrative embodiment; and

[0013] FIG. 5 is a flowchart outlining an example operation of a QA system
implementing an implicit duration annotation mechanism in accordance with
one illustrative embodiment.

DETAILED DESCRIPTION

[0014] The illustrative embodiments provide mechanisms for performing
implicit duration calculations and similarity comparisons. Moreover, the
mechanisms of the illustrative embodiments utilize such implicit duration
calculations and similarity comparisons for purposes of answering
questions using a Question Answering (QA) system, such as the IBM
Watson® QA system available from International Business Machines (IBM)
Corporation of Armonk, N.Y., or otherwise providing knowledge and
recommendations based on a knowledge system operation.

[0015] Often documents have dates and times associated with them and may
specify events, conditions, and the like, which have dates and times
associated with them. For example, in the medical domain, doctors often
make clinical notes regarding the patients that they treat, the clinical
notes serve as a patient medical history specifying the symptoms
experienced by the patient, the illnesses with which the patient is
diagnosed, medications and treatments administered or prescribed to the
patient, and the like. These clinical notes generally have dates and
times associated with them. While there are explicit dates/times
associated with such notes, durations are not generally explicitly stated
in such documents, clinical notes, etc. To the contrary, durations are
implicit in nature, leaving it to the reader to determine for themselves
what the duration is by evaluating multiple dates/times for multiple
documents, notes or entries in the documents, etc. However, durations may
be important to know in order to make decisions, answer questions, and
the like. For example, in the medical domain, medical policies are
generally dependent upon duration of an illness, time the patient has
been utilizing a particular treatment, medication, or has had some type
of medical intervention.

[0016] The implicit nature of durations in such documents can be
problematic with automated systems since it is not always readily
apparent which dates/times are related to one another such that a
duration may be determined. As such, in knowledge systems utilizing
natural language processing, such as a QA system or the like, the
knowledge systems do not take into account dates in different sections of
a document or create an association of concepts such that implicit
durations may be utilized as a basis for performing intelligent
processing.

[0017] The illustrative embodiments provide automated mechanisms for
analyzing dates/times in different portions of one or more documents, as
well as their associated text, utilizing natural language processing
techniques, to calculate implicit durations in the documents Annotations
are added to the documents to specify these calculated implicit
durations, i.e. durations that are not explicitly stated in the documents
but may be inferred from the dates/times specified in different portions
of the document or documents. The annotations correlate the calculated
implicit durations with particular associated concepts identified from
the natural language text of the document. These annotations may then be
used by the knowledge system, e.g., a QA system, to perform duration
similarity comparisons when needed to make decisions, recommendations, or
provide information to a user in response to a request from the user,
e.g., a question submitted by a user to the QA system.

[0018] For purposes of the following description, it will be assumed that
the mechanisms of the illustrative embodiments operate in a medical
domain and operate on patient history documents, e.g., electronic medical
records (EMRs), comprising clinical notes of one or more doctors,
physicians, nurses, or other medical personnel. While the illustrative
embodiments will be described in the context of a medical domain, it
should be appreciated that this is only an example and the present
invention is not limited to any particular domain. To the contrary, the
illustrative embodiments may be utilized with any domain in which
durations may be implicit in the documentation of the particular domain
such that the mechanisms of the illustrative embodiments may be utilized
to calculate the implicit durations and utilize them for purposes of
duration similarity comparisons such that knowledge system operations,
decisions, and the like may be made.

[0019] In one illustrative embodiment, a QA system is provided for
answering questions regarding the medical condition, treatment, or the
like, of patients based on their patient medical histories or EMRs. For
example, the QA system may process the patient medical history to
investigate the dates/times associated with clinical notes in the
patient's medical history and calculate implicit durations and
corresponding concepts from the patient's medical history. These implicit
durations and corresponding concepts may be compared to medical policies
to determine whether particular medical policies corresponding to the
concepts are triggered based on the calculated implicit duration. For
example, if it is determined that a patient has been receiving medication
X for at least 3 weeks (determined from a date/time on a clinical note
indicating the start of the treatment and a date/time on a clinical note
indicating that the patient is still taking the medication X on a day at
least 3 weeks later), a medical policy may state that if the mediation X
has been used for 3 weeks and the patient is still experiencing the
symptoms, then medication Y should be started. Thus, as a result of the
comparison of the calculated implicit duration of 3 weeks to the duration
in the medical policy, a corresponding recommendation for further
treatment of the patient may be generated and output by the QA system.
This will be described in greater detail hereafter.

[0020] As noted above, while the illustrative embodiments will assume that
the mechanisms operate on documents that are patient medical histories
and medical policy documents, the illustrative embodiments in other
domains may operate on other types of documents in which dates/times that
indicate implicit durations may be provided. It should be appreciated
that the term "document" as it is used herein refers to any portion of
text, provided in an electronic manner, that may be processed using a
natural language processing technique to extract facts and meaning from
the text. As such, a "document" may be a single sentence, a paragraph, a
few paragraphs, multiple pages of text, an entire book or multi-page
document, or the like. The "documents" may be provided in many different
electronic forms including being provided as document files, webpages,
individual posts to webpages or on-line forums, portions of text stored
in a database, or any other form of electronic/data representation of
text.

[0021] Before beginning a more detailed discussion of the various aspects
of the illustrative embodiments, it should first be appreciated that
throughout this description the term "mechanism" will be used to refer to
elements of the present invention that perform various operations,
functions, and the like. A "mechanism," as the term is used herein, may
be an implementation of the functions or aspects of the illustrative
embodiments in the form of an apparatus, a procedure, or a computer
program product. In the case of a procedure, the procedure is implemented
by one or more devices, apparatus, computers, data processing systems, or
the like. In the case of a computer program product, the logic
represented by computer code or instructions embodied in or on the
computer program product is executed by one or more hardware devices in
order to implement the functionality or perform the operations associated
with the specific "mechanism." Thus, the mechanisms described herein may
be implemented as specialized hardware, software executing on general
purpose hardware, software instructions stored on a medium such that the
instructions are readily executable by specialized or general purpose
hardware, a procedure or method for executing the functions, or a
combination of any of the above.

[0022] The present description and claims may make use of the terms "a",
"at least one of", and "one or more of" with regard to particular
features and elements of the illustrative embodiments. It should be
appreciated that these terms and phrases are intended to state that there
is at least one of the particular feature or element present in the
particular illustrative embodiment, but that more than one can also be
present. That is, these terms/phrases are not intended to limit the
description or claims to a single feature/element being present or
require that a plurality of such features/elements be present. To the
contrary, these terms/phrases only require at least a single
feature/element with the possibility of a plurality of such
features/elements being within the scope of the description and claims.

[0023] In addition, it should be appreciated that the following
description uses a plurality of various examples for various elements of
the illustrative embodiments to further illustrate example
implementations of the illustrative embodiments and to aid in the
understanding of the mechanisms of the illustrative embodiments. These
examples intended to be non-limiting and are not exhaustive of the
various possibilities for implementing the mechanisms of the illustrative
embodiments. It will be apparent to those of ordinary skill in the art in
view of the present description that there are many other alternative
implementations for these various elements that may be utilized in
addition to, or in replacement of, the examples provided herein without
departing from the spirit and scope of the present invention.

[0024] The present invention may be a system, a method, and/or a computer
program product. The computer program product may include a computer
readable storage medium (or media) having computer readable program
instructions thereon for causing a processor to carry out aspects of the
present invention.

[0025] The computer readable storage medium can be a tangible device that
can retain and store instructions for use by an instruction execution
device. The computer readable storage medium may be, for example, but is
not limited to, an electronic storage device, a magnetic storage device,
an optical storage device, an electromagnetic storage device, a
semiconductor storage device, or any suitable combination of the
foregoing. A non-exhaustive list of more specific examples of the
computer readable storage medium includes the following: a portable
computer diskette, a hard disk, a random access memory (RAM), a read-only
memory (ROM), an erasable programmable read-only memory (EPROM or Flash
memory), a static random access memory (SRAM), a portable compact disc
read-only memory (CD-ROM), a digital versatile disk (DVD), a memory
stick, a floppy disk, a mechanically encoded device such as punch-cards
or raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves propagating
through a waveguide or other transmission media (e.g., light pulses
passing through a fiber-optic cable), or electrical signals transmitted
through a wire.

[0026] Computer readable program instructions described herein can be
downloaded to respective computing/processing devices from a computer
readable storage medium or to an external computer or external storage
device via a network, for example, the Internet, a local area network, a
wide area network and/or a wireless network. The network may comprise
copper transmission cables, optical transmission fibers, wireless
transmission, routers, firewalls, switches, gateway computers and/or edge
servers. A network adapter card or network interface in each
computing/processing device receives computer readable program
instructions from the network and forwards the computer readable program
instructions for storage in a computer readable storage medium within the
respective computing/processing device.

[0027] Computer readable program instructions for carrying out operations
of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine instructions,
machine dependent instructions, microcode, firmware instructions,
state-setting data, or either source code or object code written in any
combination of one or more programming languages, including an object
oriented programming language such as Java, Smalltalk, C++ or the like,
and conventional procedural programming languages, such as the "C"
programming language or similar programming languages. The computer
readable program instructions may execute entirely on the user's
computer, partly on the user's computer, as a stand-alone software
package, partly on the user's computer and partly on a remote computer or
entirely on the remote computer or server. In the latter scenario, the
remote computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area network
(WAN), or the connection may be made to an external computer (for
example, through the Internet using an Internet Service Provider). In
some embodiments, electronic circuitry including, for example,
programmable logic circuitry, field-programmable gate arrays (FPGA), or
programmable logic arrays (PLA) may execute the computer readable program
instructions by utilizing state information of the computer readable
program instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.

[0028] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of methods,
apparatus (systems), and computer program products according to
embodiments of the invention. It will be understood that each block of
the flowchart illustrations and/or block diagrams, and combinations of
blocks in the flowchart illustrations and/or block diagrams, can be
implemented by computer readable program instructions.

[0029] These computer readable program instructions may be provided to a
processor of a general purpose computer, special purpose computer, or
other programmable data processing apparatus to produce a machine, such
that the instructions, which execute via the processor of the computer or
other programmable data processing apparatus, create means for
implementing the functions/acts specified in the flowchart and/or block
diagram block or blocks. These computer readable program instructions may
also be stored in a computer readable storage medium that can direct a
computer, a programmable data processing apparatus, and/or other devices
to function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an article of
manufacture including instructions which implement aspects of the
function/act specified in the flowchart and/or block diagram block or
blocks.

[0030] The computer readable program instructions may also be loaded onto
a computer, other programmable data processing apparatus, or other device
to cause a series of operational steps to be performed on the computer,
other programmable apparatus or other device to produce a computer
implemented process, such that the instructions which execute on the
computer, other programmable apparatus, or other device implement the
functions/acts specified in the flowchart and/or block diagram block or
blocks.

[0031] The flowchart and block diagrams in the Figures illustrate the
architecture, functionality, and operation of possible implementations of
systems, methods, and computer program products according to various
embodiments of the present invention. In this regard, each block in the
flowchart or block diagrams may represent a module, segment, or portion
of instructions, which comprises one or more executable instructions for
implementing the specified logical function(s). In some alternative
implementations, the functions noted in the block may occur out of the
order noted in the figures. For example, two blocks shown in succession
may, in fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of the
block diagrams and/or flowchart illustration, and combinations of blocks
in the block diagrams and/or flowchart illustration, can be implemented
by special purpose hardware-based systems that perform the specified
functions or acts or carry out combinations of special purpose hardware
and computer instructions.

[0032] As noted above, the illustrative embodiments may be implemented in,
or in association with, a knowledge system such as a QA system or the
like, so as to provide functionality for calculating durations that are
implicitly present in documentation so as to annotate the documentation
with explicit identifications of the durations and their associated
concepts. The mechanisms of the illustrative embodiments further utilize
the annotations to facilitate decision making, question answering,
information retrieval and presentation, and the like. Thus, the
illustrative embodiments may be utilized in many different types of data
processing environments providing knowledge systems utilizing natural
language processing. For purposes of the following description, it will
be assumed for example purposes only, that the present invention is
implemented in, or in association with, a QA system and are used to
generate answers to submitted questions. It should be appreciated that
the present invention may be used with other types of knowledge systems
without departing from the spirit and scope of the illustrative
embodiments.

[0033] In order to provide a context for the description of the specific
elements and functionality of the illustrative embodiments, FIGS. 1-3 are
provided hereafter as example environments in which aspects of the
illustrative embodiments may be implemented. It should be appreciated
that FIGS. 1-3 are only examples and are not intended to assert or imply
any limitation with regard to the environments in which aspects or
embodiments of the present invention may be implemented. Many
modifications to the depicted environments may be made without departing
from the spirit and scope of the present invention.

[0034] FIGS. 1-3 are directed to describing an example Question Answering
(QA) system (also referred to as a Question/Answer system or Question and
Answer system), methodology, and computer program product with which the
mechanisms of the illustrative embodiments are implemented. As will be
discussed in greater detail hereafter, the illustrative embodiments are
integrated in, augment, and extend the functionality of these QA
mechanisms with regard to implicit duration identification, duration
calculations and annotation of documents, and implicit duration based
question answering or other knowledge based decisions and operations.
While a QA system will be used in the following description of example
illustrative embodiments, it should be appreciated that the mechanisms of
the illustrative embodiments may be employed in any data processing
system implementing natural language processing in which evaluation of
implied durations is of use in presenting information, making decisions,
generating recommendations, or the other higher level logical processing.

[0035] Thus, it is important to first have an understanding of how
question and answer creation in a QA system is implemented before
describing how the mechanisms of the illustrative embodiments are
integrated in and augment such QA systems. It should be appreciated that
the QA mechanisms described in FIGS. 1-3 are only examples and are not
intended to state or imply any limitation with regard to the type of QA
mechanisms with which the illustrative embodiments are implemented. Many
modifications to the example QA system shown in FIGS. 1-3 may be
implemented in various embodiments of the present invention without
departing from the spirit and scope of the present invention.

[0036] As an overview, a Question Answering system (QA system) is an
artificial intelligence application executing on data processing hardware
that answers questions pertaining to a given subject-matter domain
presented in natural language. The QA system receives inputs from various
sources including input over a network, a corpus of electronic documents
or other data, data from a content creator, information from one or more
content users, and other such inputs from other possible sources of
input. Data storage devices store the corpus of data. A content creator
creates content in a document for use as part of a corpus of data with
the QA system. The document may include any file, text, article, or
source of data for use in the QA system. For example, a QA system
accesses a body of knowledge about the domain, or subject matter area,
e.g., financial domain, medical domain, legal domain, etc., where the
body of knowledge (knowledgebase) can be organized in a variety of
configurations, e.g., a structured repository of domain-specific
information, such as ontologies, or unstructured data related to the
domain, or a collection of natural language documents about the domain.

[0037] Content users input questions to the QA system which then answers
the input questions using the content in the corpus of data by evaluating
documents, sections of documents, portions of data in the corpus, or the
like. When a process evaluates a given section of a document for semantic
content, the process can use a variety of conventions to query such
document from the QA system, e.g., sending the query to the QA system as
a well-formed question which are then interpreted by the QA system and a
response is provided containing one or more answers to the question.
Semantic content is content based on the relation between signifiers,
such as words, phrases, signs, and symbols, and what they stand for,
their denotation, or connotation. In other words, semantic content is
content that interprets an expression, such as by using Natural Language
Processing.

[0038] As will be described in greater detail hereafter, the QA system
receives an input question, parses the question to extract the major
features of the question, uses the extracted features to formulate
queries, and then applies those queries to the corpus of data. Based on
the application of the queries to the corpus of data, the QA system
generates a set of hypotheses, or candidate answers to the input
question, by looking across the corpus of data for portions of the corpus
of data that have some potential for containing a valuable response to
the input question. The QA system then performs deep analysis on the
language of the input question and the language used in each of the
portions of the corpus of data found during the application of the
queries using a variety of reasoning algorithms. There may be hundreds or
even thousands of reasoning algorithms applied, each of which performs
different analysis, e.g., comparisons, natural language analysis, lexical
analysis, or the like, and generates a score. For example, some reasoning
algorithms may look at the matching of terms and synonyms within the
language of the input question and the found portions of the corpus of
data. Other reasoning algorithms may look at temporal or spatial features
in the language, while others may evaluate the source of the portion of
the corpus of data and evaluate its veracity.

[0039] The scores obtained from the various reasoning algorithms indicate
the extent to which the potential response is inferred by the input
question based on the specific area of focus of that reasoning algorithm.
Each resulting score is then weighted against a statistical model. The
statistical model captures how well the reasoning algorithm performed at
establishing the inference between two similar passages for a particular
domain during the training period of the QA system. The statistical model
is used to summarize a level of confidence that the QA system has
regarding the evidence that the potential response, i.e. candidate
answer, is inferred by the question. This process is repeated for each of
the candidate answers until the QA system identifies candidate answers
that surface as being significantly stronger than others and thus,
generates a final answer, or ranked set of answers, for the input
question.

[0040] As mentioned above, QA systems and mechanisms operate by accessing
information from a corpus of data or information (also referred to as a
corpus of content), analyzing it, and then generating answer results
based on the analysis of this data. Accessing information from a corpus
of data typically includes: a database query that answers questions about
what is in a collection of structured records, and a search that delivers
a collection of document links in response to a query against a
collection of unstructured data (text, markup language, etc.).
Conventional question answering systems are capable of generating answers
based on the corpus of data and the input question, verifying answers to
a collection of questions for the corpus of data, correcting errors in
digital text using a corpus of data, and selecting answers to questions
from a pool of potential answers, i.e. candidate answers.

[0041] Content creators, such as article authors, electronic document
creators, web page authors, document database creators, and the like,
determine use cases for products, solutions, and services described in
such content before writing their content. Consequently, the content
creators know what questions the content is intended to answer in a
particular topic addressed by the content. Categorizing the questions,
such as in terms of roles, type of information, tasks, or the like,
associated with the question, in each document of a corpus of data allows
the QA system to more quickly and efficiently identify documents
containing content related to a specific query. The content may also
answer other questions that the content creator did not contemplate that
may be useful to content users. The questions and answers may be verified
by the content creator to be contained in the content for a given
document. These capabilities contribute to improved accuracy, system
performance, machine learning, and confidence of the QA system. Content
creators, automated tools, or the like, annotate or otherwise generate
metadata for providing information useable by the QA system to identify
these question and answer attributes of the content.

[0042] Operating on such content, the QA system generates answers for
input questions using a plurality of intensive analysis mechanisms which
evaluate the content to identify the most probable answers, i.e.
candidate answers, for the input question. The most probable answers are
output as a ranked listing of candidate answers ranked according to their
relative scores or confidence measures calculated during evaluation of
the candidate answers, as a single final answer having a highest ranking
score or confidence measure, or which is a best match to the input
question, or a combination of ranked listing and final answer.

[0043] FIG. 1 depicts a schematic diagram of one illustrative embodiment
of a question/answer creation (QA) system 100 in a computer network 102.
One example of a question/answer generation which may be used in
conjunction with the principles described herein is described in U.S.
Patent Application Publication No. 2011/0125734, which is herein
incorporated by reference in its entirety. The QA system 100 is
implemented on one or more computing devices 104 (comprising one or more
processors and one or more memories, and potentially any other computing
device elements generally known in the art including buses, storage
devices, communication interfaces, and the like) connected to the
computer network 102. The network 102 includes multiple computing devices
104 in communication with each other and with other devices or components
via one or more wired and/or wireless data communication links, where
each communication link comprises one or more of wires, routers,
switches, transmitters, receivers, or the like. The QA system 100 and
network 102 enables question/answer (QA) generation functionality for one
or more QA system users via their respective computing devices 110-112.
Other embodiments of the QA system 100 may be used with components,
systems, sub-systems, and/or devices other than those that are depicted
herein.

[0044] The QA system 100 is configured to implement a QA system pipeline
108 that receive inputs from various sources. For example, the QA system
100 receives input from the network 102, a corpus of electronic documents
106, QA system users, and/or other data and other possible sources of
input. In one embodiment, some or all of the inputs to the QA system 100
are routed through the network 102. The various computing devices 104 on
the network 102 include access points for content creators and QA system
users. Some of the computing devices 104 include devices for a database
storing the corpus of data 106 (which is shown as a separate entity in
FIG. 1 for illustrative purposes only). Portions of the corpus of data
106 may also be provided on one or more other network attached storage
devices, in one or more databases, or other computing devices not
explicitly shown in FIG. 1. The network 102 includes local network
connections and remote connections in various embodiments, such that the
QA system 100 may operate in environments of any size, including local
and global, e.g., the Internet.

[0045] In one embodiment, the content creator creates content in a
document of the corpus of data 106 for use as part of a corpus of data
with the QA system 100. The document includes any file, text, article, or
source of data for use in the QA system 100. QA system users access the
QA system 100 via a network connection or an Internet connection to the
network 102, and input questions to the QA system 100 that are answered
by the content in the corpus of data 106. In one embodiment, the
questions are formed using natural language. The QA system 100 parses and
interprets the question, and provides a response to the QA system user,
e.g., QA system user 110, containing one or more answers to the question.
In some embodiments, the QA system 100 provides a response to users in a
ranked list of candidate answers while in other illustrative embodiments,
the QA system 100 provides a single final answer or a combination of a
final answer and ranked listing of other candidate answers.

[0046] The QA system 100 implements a QA system pipeline 108 which
comprises a plurality of stages for processing an input question and the
corpus of data 106. The QA system pipeline 108 generates answers for the
input question based on the processing of the input question and the
corpus of data 106. The QA system pipeline 108 will be described in
greater detail hereafter with regard to FIG. 3.

[0047] In some illustrative embodiments, the QA system 100 may be the IBM
Watson® QA system available from International Business Machines
Corporation of Armonk, N.Y., which is augmented with the mechanisms of
the illustrative embodiments described hereafter. As outlined previously,
the IBM Watson® QA system receives an input question which it then
parses to extract the major features of the question, that in turn are
then used to formulate queries that are applied to the corpus of data.
Based on the application of the queries to the corpus of data, a set of
hypotheses, or candidate answers to the input question, are generated by
looking across the corpus of data for portions of the corpus of data that
have some potential for containing a valuable response to the input
question. The IBM Watson® QA system then performs deep analysis on the
language of the input question and the language used in each of the
portions of the corpus of data found during the application of the
queries using a variety of reasoning algorithms. The scores obtained from
the various reasoning algorithms are then weighted against a statistical
model that summarizes a level of confidence that the IBM Watson® QA
system has regarding the evidence that the potential response, i.e.
candidate answer, is inferred by the question. This process is be
repeated for each of the candidate answers to generate ranked listing of
candidate answers which may then be presented to the user that submitted
the input question, or from which a final answer is selected and
presented to the user. More information about the IBM Watson® QA
system may be obtained, for example, from the IBM Corporation website,
IBM Redbooks, and the like. For example, information about the IBM
Watson® QA system can be found in Yuan et al., "Watson and
Healthcare," IBM developerWorks, 2011 and "The Era of Cognitive Systems:
An Inside Look at IBM Watson and How it Works" by Rob High, IBM Redbooks,
2012.

[0048] As shown in FIG. 1, with particular importance to the mechanisms of
the illustrative embodiments, the QA system 100 further comprises an
implicit duration engine 120 that operates in conjunction with the QA
system pipeline 108. In operating with the QA system pipeline 108, the
implicit duration engine 120 operates on the corpus or corpora of
documents associated with the QA system pipeline 108, during ingestion of
the corpus or corpora or as a pre-processor prior to ingestion, to
analyze the documents and identify implicit durations within, and
between, these documents along with the corresponding concepts associated
with these implicit durations, e.g., administration of a treatment
(concept) for a medical condition (concept) being provided for one month
(duration) as implicitly defined by the specification of multiple
dates/times within, or between, the documents. These implicit durations
are identified and the actual duration is calculated and made explicit
through annotation of the documents in the corpus or corpora which
associates the calculated duration with the concepts identified in the
natural language documents of the corpus or corpora and makes these
explicit in the annotation metadata associated with the documents.

[0049] FIG. 2 is a block diagram of an example data processing system in
which aspects of the illustrative embodiments are implemented. Data
processing system 200 is an example of a computer, such as server 104 or
client 110 in FIG. 1, in which computer usable code or instructions
implementing the processes for illustrative embodiments of the present
invention are located. In one illustrative embodiment, FIG. 2 represents
a server computing device, such as a server 104, which, which implements
a QA system 100 and QA system pipeline 108 augmented to include the
additional mechanisms of the illustrative embodiments described
hereafter.

[0053] An operating system runs on processing unit 206. The operating
system coordinates and provides control of various components within the
data processing system 200 in FIG. 2. As a client, the operating system
is a commercially available operating system such as Microsoft®
Windows 8®. An object-oriented programming system, such as the
Java® programming system, may run in conjunction with the operating
system and provides calls to the operating system from Java® programs
or applications executing on data processing system 200.

[0054] As a server, data processing system 200 may be, for example, an
IBM® eServer® System p® computer system, running the Advanced
Interactive Executive (AIX®) operating system or the LINUX®
operating system. Data processing system 200 may be a symmetric
multiprocessor (SMP) system including a plurality of processors in
processing unit 206. Alternatively, a single processor system may be
employed.

[0055] Instructions for the operating system, the object-oriented
programming system, and applications or programs are located on storage
devices, such as HDD 226, and are loaded into main memory 208 for
execution by processing unit 206. The processes for illustrative
embodiments of the present invention are performed by processing unit 206
using computer usable program code, which is located in a memory such as,
for example, main memory 208, ROM 224, or in one or more peripheral
devices 226 and 230, for example.

[0056] A bus system, such as bus 238 or bus 240 as shown in FIG. 2, is
comprised of one or more buses. Of course, the bus system may be
implemented using any type of communication fabric or architecture that
provides for a transfer of data between different components or devices
attached to the fabric or architecture. A communication unit, such as
modem 222 or network adapter 212 of FIG. 2, includes one or more devices
used to transmit and receive data. A memory may be, for example, main
memory 208, ROM 224, or a cache such as found in NB/MCH 202 in FIG. 2.

[0057] Those of ordinary skill in the art will appreciate that the
hardware depicted in FIGS. 1 and 2 may vary depending on the
implementation. Other internal hardware or peripheral devices, such as
flash memory, equivalent non-volatile memory, or optical disk drives and
the like, may be used in addition to or in place of the hardware depicted
in FIGS. 1 and 2. Also, the processes of the illustrative embodiments may
be applied to a multiprocessor data processing system, other than the SMP
system mentioned previously, without departing from the spirit and scope
of the present invention.

[0058] Moreover, the data processing system 200 may take the form of any
of a number of different data processing systems including client
computing devices, server computing devices, a tablet computer, laptop
computer, telephone or other communication device, a personal digital
assistant (PDA), or the like. In some illustrative examples, data
processing system 200 may be a portable computing device that is
configured with flash memory to provide non-volatile memory for storing
operating system files and/or user-generated data, for example.
Essentially, data processing system 200 may be any known or later
developed data processing system without architectural limitation.

[0059] FIG. 3 illustrates a QA system pipeline for processing an input
question in accordance with one illustrative embodiment. The QA system
pipeline of FIG. 3 may be implemented, for example, as QA system pipeline
108 of QA system 100 in FIG. 1. It should be appreciated that the stages
of the QA system pipeline shown in FIG. 3 are implemented as one or more
software engines, components, or the like, which are configured with
logic for implementing the functionality attributed to the particular
stage. Each stage is implemented using one or more of such software
engines, components or the like. The software engines, components, etc.
are executed on one or more processors of one or more data processing
systems or devices and utilize or operate on data stored in one or more
data storage devices, memories, or the like, on one or more of the data
processing systems. The QA system pipeline of FIG. 3 is augmented, for
example, in one or more of the stages to implement the improved mechanism
of the illustrative embodiments described hereafter, additional stages
may be provided to implement the improved mechanism, or separate logic
from the pipeline 300 may be provided for interfacing with the pipeline
300 and implementing the improved functionality and operations of the
illustrative embodiments.

[0060] As shown in FIG. 3, the QA system pipeline 300 comprises a
plurality of stages 310-380 through which the QA system operates to
analyze an input question and generate a final response. In an initial
question input stage 310, the QA system receives an input question that
is presented in a natural language format. That is, a user inputs, via a
user interface, an input question for which the user wishes to obtain an
answer, e.g., "Who are Washington's closest advisors?" In response to
receiving the input question, the next stage of the QA system pipeline
300, i.e. the question and topic analysis stage 320, parses the input
question using natural language processing (NLP) techniques to extract
major features from the input question, and classify the major features
according to types, e.g., names, dates, or any of a plethora of other
defined topics. For example, in the example question above, the term
"who" may be associated with a topic for "persons" indicating that the
identity of a person is being sought, "Washington" may be identified as a
proper name of a person with which the question is associated, "closest"
may be identified as a word indicative of proximity or relationship, and
"advisors" may be indicative of a noun or other language topic.

[0061] In addition, the extracted major features include key words and
phrases classified into question characteristics, such as the focus of
the question, the lexical answer type (LAT) of the question, and the
like. As referred to herein, a lexical answer type (LAT) is a word in, or
a word inferred from, the input question that indicates the type of the
answer, independent of assigning semantics to that word. For example, in
the question "What maneuver was invented in the 1500s to speed up the
game and involves two pieces of the same color?," the LAT is the string
"maneuver." The focus of a question is the part of the question that, if
replaced by the answer, makes the question a standalone statement. For
example, in the question "What drug has been shown to relieve the
symptoms of ADD with relatively few side effects?," the focus is "drug"
since if this word were replaced with the answer, e.g., the answer
"Adderall" can be used to replace the term "drug" to generate the
sentence "Adderall has been shown to relieve the symptoms of ADD with
relatively few side effects." The focus often, but not always, contains
the LAT. On the other hand, in many cases it is not possible to infer a
meaningful LAT from the focus.

[0062] Referring again to FIG. 3, the identified major features are then
used during the question decomposition stage 330 to decompose the
question into one or more queries that are applied to the corpora of
data/information 345 in order to generate one or more hypotheses. The
queries are generated in any known or later developed query language,
such as the Structure Query Language (SQL), or the like. The queries are
applied to one or more databases storing information about the electronic
texts, documents, articles, websites, and the like, that make up the
corpora of data/information 345. That is, these various sources
themselves, different collections of sources, and the like, represent a
different corpus 347 within the corpora 345. There may be different
corpora 347 defined for different collections of documents based on
various criteria depending upon the particular implementation. For
example, different corpora may be established for different topics,
subject matter categories, sources of information, or the like. As one
example, a first corpus may be associated with healthcare documents while
a second corpus may be associated with financial documents.
Alternatively, one corpus may be documents published by the U.S.
Department of Energy while another corpus may be IBM Redbooks documents.
Any collection of content having some similar attribute may be considered
to be a corpus 347 within the corpora 345.

[0063] The queries are applied to one or more databases storing
information about the electronic texts, documents, articles, websites,
and the like, that make up the corpus of data/information, e.g., the
corpus of data 106 in FIG. 1. The queries are applied to the corpus of
data/information at the hypothesis generation stage 340 to generate
results identifying potential hypotheses for answering the input
question, which can then be evaluated. That is, the application of the
queries results in the extraction of portions of the corpus of
data/information matching the criteria of the particular query. These
portions of the corpus are then analyzed and used, during the hypothesis
generation stage 340, to generate hypotheses for answering the input
question. These hypotheses are also referred to herein as "candidate
answers" for the input question. For any input question, at this stage
340, there may be hundreds of hypotheses or candidate answers generated
that may need to be evaluated.

[0064] The QA system pipeline 300, in stage 350, then performs a deep
analysis and comparison of the language of the input question and the
language of each hypothesis or "candidate answer," as well as performs
evidence scoring to evaluate the likelihood that the particular
hypothesis is a correct answer for the input question. As mentioned
above, this involves using a plurality of reasoning algorithms, each
performing a separate type of analysis of the language of the input
question and/or content of the corpus that provides evidence in support
of, or not in support of, the hypothesis. Each reasoning algorithm
generates a score based on the analysis it performs which indicates a
measure of relevance of the individual portions of the corpus of
data/information extracted by application of the queries as well as a
measure of the correctness of the corresponding hypothesis, i.e. a
measure of confidence in the hypothesis. There are various ways of
generating such scores depending upon the particular analysis being
performed. In generally, however, these algorithms look for particular
terms, phrases, or patterns of text that are indicative of terms,
phrases, or patterns of interest and determine a degree of matching with
higher degrees of matching being given relatively higher scores than
lower degrees of matching.

[0065] Thus, for example, an algorithm may be configured to look for the
exact term from an input question or synonyms to that term in the input
question, e.g., the exact term or synonyms for the term "movie," and
generate a score based on a frequency of use of these exact terms or
synonyms. In such a case, exact matches will be given the highest scores,
while synonyms may be given lower scores based on a relative ranking of
the synonyms as may be specified by a subject matter expert (person with
knowledge of the particular domain and terminology used) or automatically
determined from frequency of use of the synonym in the corpus
corresponding to the domain. Thus, for example, an exact match of the
term "movie" in content of the corpus (also referred to as evidence, or
evidence passages) is given a highest score. A synonym of movie, such as
"motion picture" may be given a lower score but still higher than a
synonym of the type "film" or "moving picture show." Instances of the
exact matches and synonyms for each evidence passage may be compiled and
used in a quantitative function to generate a score for the degree of
matching of the evidence passage to the input question.

[0066] Thus, for example, a hypothesis or candidate answer to the input
question of "What was the first movie?" is "The Horse in Motion." If the
evidence passage contains the statements "The first motion picture ever
made was `The Horse in Motion` in 1878 by Eadweard Muybridge. It was a
movie of a horse running," and the algorithm is looking for exact matches
or synonyms to the focus of the input question, i.e. "movie," then an
exact match of "movie" is found in the second sentence of the evidence
passage and a highly scored synonym to "movie," i.e. "motion picture," is
found in the first sentence of the evidence passage. This may be combined
with further analysis of the evidence passage to identify that the text
of the candidate answer is present in the evidence passage as well, i.e.
"The Horse in Motion." These factors may be combined to give this
evidence passage a relatively high score as supporting evidence for the
candidate answer "The Horse in Motion" being a correct answer.

[0067] It should be appreciated that this is just one simple example of
how scoring can be performed. Many other algorithms of various complexity
may be used to generate scores for candidate answers and evidence without
departing from the spirit and scope of the present invention.

[0068] In the synthesis stage 360, the large number of scores generated by
the various reasoning algorithms are synthesized into confidence scores
or confidence measures for the various hypotheses. This process involves
applying weights to the various scores, where the weights have been
determined through training of the statistical model employed by the QA
system and/or dynamically updated. For example, the weights for scores
generated by algorithms that identify exactly matching terms and synonym
may be set relatively higher than other algorithms that are evaluating
publication dates for evidence passages. The weights themselves may be
specified by subject matter experts or learned through machine learning
processes that evaluate the significance of characteristics evidence
passages and their relative importance to overall candidate answer
generation.

[0069] The weighted scores are processed in accordance with a statistical
model generated through training of the QA system that identifies a
manner by which these scores may be combined to generate a confidence
score or measure for the individual hypotheses or candidate answers. This
confidence score or measure summarizes the level of confidence that the
QA system has about the evidence that the candidate answer is inferred by
the input question, i.e. that the candidate answer is the correct answer
for the input question.

[0070] The resulting confidence scores or measures are processed by a
final confidence merging and ranking stage 370 which compares the
confidence scores and measures to each other, compares them against
predetermined thresholds, or performs any other analysis on the
confidence scores to determine which hypotheses/candidate answers are the
most likely to be the correct answer to the input question. The
hypotheses/candidate answers are ranked according to these comparisons to
generate a ranked listing of hypotheses/candidate answers (hereafter
simply referred to as "candidate answers"). From the ranked listing of
candidate answers, at stage 380, a final answer and confidence score, or
final set of candidate answers and confidence scores, are generated and
output to the submitter of the original input question via a graphical
user interface or other mechanism for outputting information.

[0071] As discussed above, the illustrative embodiments augment the
operation of the QA system pipeline 300 with the additional functionality
of the implicit duration engine, which is shown in FIG. 3 as implicit
duration engine 390. The implicit duration engine 390 comprises document
implicit duration analysis logic 392, document duration annotation logic
394, and duration comparison logic 396. In addition, the implicit
duration engine 390 includes, or operates in concert with, a policy
database which, in this example implementation, is a medical policy
database 398. The implicit duration engine 390 works in conjunction with
various stages of the QA system pipeline, such as stages 340 and 350, to
assist in the generation of candidate answers to an input question 310 by
evaluating implicit durations in documents (made explicit through the
operation of the implicit duration engine 390) and comparing the implicit
durations to criteria for generation of candidate answers as described
hereafter. It should be appreciated that while FIG. 3 illustrates the
duration comparison logic 396 and medical policy database 398 as being
part of the implicit duration engine 390, this logic may be integrated
into the logic of other stages of the QA system pipeline 300, such as
hypothesis generation stage 340, hypothesis and evidence scoring stage
350, or the like, without departing from the spirit and scope of the
illustrative embodiments.

[0072] The implicit duration engine 390 receives a document, or collection
of related documents, for processing, such as part of a corpus 347 or
corpora 345 ingestion operation of the QA system. The document is
analyzed by the implicit duration analysis logic 392 to identify
dates/times associated with the document as a whole, e.g. publication
dates/times, creation dates/times, dates/times mentioned in the content
of the document, or the like. These dates/times may be identified via
pattern matching, keyword identification, metadata analysis, or any other
method of identify a string of alphanumeric characters indicative of a
date/time. Natural language processing is performed by the implicit
duration analysis logic 392 on the text associated with the dates/times
identified in the document to identify concepts associated with those
dates/times. For example, concepts in text that are in close proximity to
the date/time, e.g., within a predetermined number of words, sentences,
paragraphs, etc., or that is pointed to by the date/time (in the case of
dates/times being provided in metadata linked to particular text within
the document), are identified and associated with the date/time. In one
illustrative embodiment, the natural language processing in this regard
may make use of domain-specific resources 393, such as domain-specific
dictionaries, synonym databases, and other reference data structures that
facilitate the identification of words and phrases within the text that
are indicative of domain-specific concepts. These domain specific
resources 393 may be part of the implicit duration engine 390 or may be
separate from the implicit duration engine 390 (as shown) and in some
cases may be resources used by the QA system pipeline 300 when processing
the input question 310 in the manner described above.

[0073] In one example, the domain-specific resources 393 are specific to a
medical domain, and may be specific to a particular type of medical
domain, e.g., oncology, podiatry, cardiology, etc. For example, with a
medical domain, the domain-specific resources 393 may comprise a
dictionary of medical terms/phrases, e.g., the terms "renal",
"autoimmune", "carcinoma", and a plethora of other terms/phrases in the
medical domain, which may be used to match to terms/phrases in text of
documents to identify concepts. Similarly, synonym databases may be
established and utilized for the particular medical domain such that
terms/phrases that are synonyms of the terms/phrases in the
domain-specific dictionary may be identified and related to the
domain-specific dictionary terms/phrases, e.g., "renal failure" and
"kidney failure" may be considered synonyms of each other.

[0074] In one illustrative embodiment, the documents that are processed by
the implicit duration engine 390 are patient medical records that specify
patient medical histories listing out the patient symptoms, medical
diagnosis, and treatments that the patient has experienced over time.
These patient medical histories may comprise clinical notes made by
health care professionals, e.g., doctors, nurses, medical technicians,
and the like, during the treatment of the patient for one or more various
medical conditions. The clinical notes generally have a date/time
associated with them and specify the particular symptoms, diagnosis, and
the treatment prescribed for the diagnosis. It should be appreciated that
this is but one example of the documents that may be processed by the
mechanisms of the illustrative embodiments and is not intended to be
limiting on the scope of the present invention.

[0075] Each date/time identified in the document(s) may have one or more
concepts associated with it as extracted from the text related to the
date/time by way of the natural language processing. For example,
assuming that the document is a patient's medical record, an entry in the
medical record may be of the type "10.14.14 Patient complains of
abdominal pain; prescribed laxative and ordered x-ray". From this entry
in the patient's medical record, the date/time of "10.14.14" may be
identified, which may have other formats as determined and identifiable
by the implicit duration engine 390, e.g., "14.10.14," "October 14,
2014," "14 October 2014," "Oct. 14, 2014," and the like, and the
corresponding concepts extracted from the text using natural language
processing may include "abdominal pain," "laxative," and "x-ray," for
example.

[0076] Multiple such dates/times and corresponding concepts may be
identified in a document, or from a collection of documents, so that
these multiple date/time entries may be compared to identify related
dates/times that are indicative of an implicit duration. For example,
assume that a subsequent entry is made to the patient's medical record of
"10.21.14 Patient continues to complain of abdominal pain; patient is
still taking laxative; x-ray results are negative." From this entry in
the patient's medical record, another date/time and corresponding
concepts entry may be generated that indicates the date/time of
"10.21.14" with corresponding concepts of "abdominal pain," "laxative,"
"x-ray", and "results are negative." It should be appreciated that other
documents related to the document in question may also be processed in
this manner, e.g., radiology reports, laboratory test result documents,
etc., so as to obtain a complete understanding of the events and
conditions present at various dates/times.

[0077] For example, in the above scenario, reports made for the same
patient from a x-ray technician or radiologist may be correlated with the
patient medical record to obtain an entry of the type "October 14, 2014:
x-ray of patient shows no obstructions of abdominal cavity; no other
indications of possible cause of abdominal pain; results negative." In
such a situation, the implicit duration engine 390 may analyze this entry
in another document and correlate it with the entries obtained from the
patient medical record to generate a combined and correlated document
duration data structure 395 in memory of the implicit duration engine 390
that indicates the overall dates/times and related concepts obtained from
the related documents, e.g., in this case another entry with the
date/time 10.14.14 in which the concepts of "no obstructions," "abdominal
cavity," "abdominal pain," and "results negative" is generated.

[0078] It should be appreciated that, in this scenario, there is no
explicit time duration indicated in the patient's medical record for how
long the patient has been experiencing the medical condition or how long
the patient has been undergoing the particular treatment, e.g., taking of
laxatives in this example. To the contrary, only a listing of clinical
notes with the corresponding dates/times of the clinical notes is
provided. Thus, it is difficult for automated systems to process such
documents and apply policies that are duration based policies since the
durations are not explicitly stated in the documents themselves. For
example, if a medical policy is in place that states that "If abdominal
pain persists after 5 days of laxative treatment, perform MRI on
patient," it is difficult for knowledge systems to determine whether the
policy is applicable to a particular situation when the duration (5 days)
is not explicitly stated in the input data.

[0079] Having identified date/times in the document(s) in question, and
their corresponding concepts, to generate a document duration data
structure 395, the implicit duration annotation logic 394 operates on the
document duration data structure 395 to calculate date/time durations
associated with the documents. In performing such calculations, the
implicit duration annotation logic 394 identifies entries in the document
duration data structure 395 that have similar concepts and scores these
entries according to their level of similarity. For example, the
terms/phrases representative of the concepts associated with the
dates/times in the entries of the document duration data structure 395
are compared, taking into consideration synonyms and the like, and a
degree of similarity is calculated based on the amount of matching of the
terms/phrases. Using the above scenario as an example, the initial entry
for Oct. 14, 2014 having related concepts of "abdominal pain,"
"laxative," and "x-ray," would have a relatively high confidence score
(indicating high confidence that the entries are related) when compared
to the subsequent entry for Oct. 21, 2014 with related concepts of
"abdominal pain," "laxative," "x-ray", and "results are negative." That
is, since all of the concepts in the first entry also appear in the
second entry, there is a strong relationship between the entries
indicative of the entries representing a same series of events or
conditions. As a result, these entries are identified and correlated as
being related to one another. Another entry of the type "11.15.14 Patient
complains of eye strain; referred patient to ophthalmologist" would have
a relatively low confidence score (indicating a low confidence that the
entries are related) with regard to being correlated with the Oct. 14,
2014 entry since none of the concepts expressed in the Nov. 15, 2014
entry relate to the Oct. 14, 2014 entry.

[0080] Groupings of one or more entries of this type may be made with
regard to each entry in the document duration data structure 395 to
generate confidence scores associated with each pairing or set of
entries. Such grouping of entries may be done, for example, using a
clustering approach or other grouping algorithm that identifies
commonalities between the concepts associated with the entries. One or
more thresholds may be pre-established for specifying a requisite score
indicative of a correlation of entries in the document duration data
structure 395. For example, a threshold may be set of 95% indicating that
a 95% confidence score that the entries are related is required before
the entries are determined to be related and correlated into a grouping
of entries for purposes of an annotation generation operation.

[0081] For those entries that are determined to be sufficiently related
to, and correlated with, one another such that they are grouped together,
a duration is calculated by the implicit duration annotation logic 394
based on the dates/times associated with the correlated entries. The
duration is associated with the matching concepts of the entries that are
related and correlated with one another, or a combination of the concepts
of the multiple entries. Thus, for example, in the above scenario, from
the Oct. 14, 2014 and Oct. 21, 2014 entries in the document duration data
structure 395, a duration of 7 days is generated by comparing the
dates/times (e.g., subtracting the earlier date/time from the latter
date/time). In one illustrative embodiment, this duration is associated
with the concepts of "abdominal pain", "laxative," and "x-ray" which are
the concepts that match between the two correlated entries. In other
illustrative embodiments, a maximum set of concepts from the correlated
entries is generated such that concepts that may not appear or match
between the entries may also be listed, e.g., in the Oct. 21, 2014 entry,
the concept of "results are negative" is also present but not in the Oct.
14, 2014 entry, but may be included in association with the duration
calculation.

[0082] The calculated duration and its corresponding concepts are used to
generate an annotation that is added to the metadata associated with the
document(s) being processed. That is, the implicit duration annotation
logic 394 generates an annotation that the logic 394 then inserts into
the metadata of the document to thereby make explicit the implicit
duration. This implicit duration may be set, for example, as the largest
difference in date/time associated with entries in the grouping of
entries. Of course other criteria for determining the implicit duration
may also be used without departing from the spirit and scope of the
illustrative embodiments. For example, a series of smaller durations in
addition to a largest difference may be provided. The duration may be
specified in many different formats utilizing different units of measure,
e.g., days, weeks, months, years, etc. In some cases, the duration may be
specified in association with start and end dates, years, etc., "from
January 2014 to March 2014 (3 months) the patient suffered from abdominal
pain" such that a temporal context for the duration may be made explicit
in the annotation.

[0083] It should be appreciated that there may be an implicit duration
created for each entry, or concept, to which another entry or concept is
compared and this can result in multiple implicit durations for an entry
or concept. For example, assume there are three sections in a document,
each section corresponding to a different entry, and that each section
has a date associated with it. Assume also that the dates are arranged in
these sections from earliest to latest and that there are concepts that
are in common between the sections. In such an example, there will be no
implicit durations created in the first section as it is the earliest
section and nothing is compared against the earliest section to generate
an implicit duration leading up to this earliest section. However, there
will be an implicit duration created for each concept in the second
section that is common with the first section. Moreover, for the third
section, for every concept that is in all three sections, there will be
two implicit durations created as that concept would compare to both
existing sections. For concepts that are only in common with one of the
existing sections then only one implicit duration will be created. Thus,
each of the second and third sections may have multiple implicit
durations generated and stored in association with that section and/or
the document as a whole.

[0084] It should be appreciated that if multiple documents are being
correlated in this manner, annotations may be inserted into each of the
documents or only a subset of the documents depending on the
implementation. For example, a primary document (e.g., patient medical
record document) may be designated which receives the duration
annotations while other supporting documents (e.g., radiology report, lab
results report, etc.) may not be augmented with the duration annotation.
The designation of primary and secondary documentation may be specified
in configuration parameters for the implicit duration annotation logic
394, for example, e.g., patient medical records are considered a primary
document that is to be annotated while all other documents are considered
secondary and will not be annotated.

[0085] As noted above, this process may be repeated for each document in
the particular corpus/corpora 345, 347 that is utilized by the QA system
pipeline 300 and may be done as part of a pre-processing or ingestion
process. The resulting augmented corpus/corpora 345, 347 may then be used
by the QA system pipeline 300 in a manner as previously described above
to answer input questions 310 taking into account these calculated
durations. In one illustrative embodiment, this may involve the
comparison of durations in policies, rules, or other logic statements
with the implicit durations within the documents that are now made
explicit by way of the implicit duration engine 390 providing annotations
to these documents in the corpus/corpora 345, 347. One example mechanism
that may be utilized for comparing durations and determining similarities
of durations is described in commonly assigned and co-pending U.S. patent
application Ser. No. 14/183,701 entitled "NLP Duration and Duration Range
Comparison Methodology Using Similarity Weighting," filed on Feb. 19,
2014, which is hereby incorporated by reference.

[0086] For simplicity of the present explanation, this functionality for
comparing durations of policies with durations in annotations is shown as
being part of the implicit duration engine 390, but may be integrated
into the logic of the various stages of the QA system pipeline 300
without departing from the spirit and scope of the illustrative
embodiments. The implicit duration comparison logic 396 compares the
implicit durations that are explicitly stated in the annotations of the
documents considered for generation of answers to the input question 310,
or documents that provide supporting evidence for candidate answers
generated by the QA system pipeline 300, to determine whether one or more
policies in the policy database 398 (which in this example case is a
medical policy database) are triggered by the implicit durations. As
described in the incorporated co-pending U.S. patent application Ser. No.
14/183,701, this may involve determining a similarity of durations and
generating a similarity score which may be compared to a threshold to
determine if there is a match.

[0087] Based on whether a policy is triggered by a matching implicit
duration specified in the annotations of the document, a result is
returned to one or more of the stages of the QA system pipeline 300,
e.g., stages 340 or 350, to affect the confidence scores associated with
the candidate answers generated by the QA system pipeline 300, add
additional evidence in support of or against a particular candidate
answer, or simply offer additional information to be presented along with
the candidate answers when presenting them to the original submitter of
the input question 310. For example, in one illustrative embodiment, if a
candidate answer is generated that has a particular confidence score
associated with it, but the implicit duration does not match, then the
confidence score for the candidate answer may be reset to zero, e.g., if
it is determined that in general a MRI would be recommended, but the
patient has not yet been receiving drug X for 3 weeks, then the candidate
answer of sending the patient to get an MRI will have its confidence
score reduced to zero. Other ways in which to adjust candidate answer
scoring may be to increase/decrease the candidate score, without setting
it to zero, based on the duration matching or not matching, e.g., an MRI
as a candidate answer may have its confidence score reduced, but not set
to zero, when the durations do not match, or if the difference in
durations is less than a particular threshold amount, e.g., the required
duration is 3 weeks, but the patient has only been on drug X for 2.5
weeks, then the candidate answer score for the MRI may be reduced, but
not set to zero. The amount of the reduction may correspond to the amount
of difference between the implied duration and the duration requirements,
for example.

[0088] Thus, the illustrative embodiments provide mechanisms for making
implicit durations in documents more explicit such that they may be used
as a basis for performing knowledge system operations. In particular, in
the illustrative embodiments, this allows the mechanisms of the
illustrative embodiments to determine if pre-established duration based
policies are triggered based on implicit durations in documentation.
Based on this triggering of policies, modifications to the results
returned to a user may be made to take into consideration the policies
that are triggered, e.g., a treatment may be recommended to a patient,
medical professional, or the like, for a particular medical condition
based on the triggering of a policy by an implicit duration identified in
medical documents associated with the patient. As a result, policy based
decisions, question answer, and information retrieval based on implicit
durations is facilitated.

[0089] To further illustrate the operation of the illustrative embodiments
in the context of a medical domain, consider the example shown in FIG. 4.
FIG. 4 shows an example medical policy and patient clinical history, as
may be provided in an electronic patient medical record for example, in
accordance with one illustrative embodiment. As shown in FIG. 4, the
medical policy database 410 comprises a plurality of medical policies
directed to the approval/denial of particular medical equipment and
procedures. Such a medical policy database 410 may comprise medical
policies of one or more health insurance agencies and may specify the
types of devices, treatments, medical procedures, and the like, that the
health insurance agencies approve/deny and the conditions of
approval/denial, for example. In the depicted example, one such medical
policy is a medical policy 412 that indicates that a health insurance
professional should "Approve intermittent pneumatic compression device if
edema persists despite a trial of properly fitted gradient compression
stockings for at least six weeks."

[0090] In addition, as shown in FIG. 4, the patient clinical history 420
includes entries 422, 424, and 426 for various service dates when a
medical professional attended to the patient. These entries 422-426
include dates of service and corresponding text representing clinical
notes describing the service provided, medical conditions present, or
with which the patient was diagnosed, and other information regarding the
medical situation of the patient at the time of service.

[0091] Assume that a physician wishes to prescribe to the patient an
intermittent pneumatic compression device and submits to the QA system an
input question of the type "Is the patient approved for use of an
intermittent pneumatic compression device?" The QA system may analyze and
decompose the question in the manner previously described and, as part of
the candidate answer generation, utilize the implicit duration engine of
the illustrative embodiments to determine if there is a medical policy in
the medical policy database 410 that is triggered by the implicit
durations indicated in the patient's clinical history 420 (which operates
as a document from the corpus). Prior to utilizing the implicit duration,
however, the mechanisms of the illustrative embodiments annotate the
patient's clinical history 420 with annotations 428 specifying the
implicit durations in the patient's clinical history 420 explicitly.

[0092] For example, looking at the various service entries 422-426, it can
be seen that three entries are made in the patient's clinical history 420
that have related concepts of "compression stockings" and "edema".
Grouping or otherwise correlating these entries 422-426, an annotation
428 is generated that indicates that there is an implicit duration of two
months (8 weeks or 61 days) between service visits and the patient is
still undergoing the same treatment with compression stockings and is
still exhibiting issues with edema.

[0093] When answering the input question from the physician, the
annotation 428 is compared to the medical policies in the medical policy
database 410, which may also serve as a portion of the corpus upon which
the QA system operates, to thereby identify the medical policy 412 which
relates to edema and compression stockings. This identification of a
policy 412 in the medical policy database 410 may be performed using
natural language processing and evaluation of the corpus 347, of which
the database 410 may be a part, in a manner as discussed above. Thus,
applying queries against the medical policy database 410, the QA system
may identify the medical policy 412 as being pertinent to the question
that mentions the concepts of "edema" and an "intermittent pneumatic
compression device." Comparing the implicit duration annotation 428 of
the patient clinical history 420 to the criteria of the medical policy
412, it is clear that the implicit duration associated with the time
lapse between Jan. 12, 2014 and Mar. 14, 2014 meets or exceeds the "at
least six weeks" requirement for the use of gradient compression
stockings, and thus, approval of the prescribing of the "intermittent
pneumatic compression device" is warranted. It should be noted that if
the last entry 426 were not present in the patient's clinical history
420, then the implicit duration that would result from analysis of the
patient's clinical history 420 would not meet the requirements of the
medical policy 412 since the patient would only have been using the
compression stockings for approximately four weeks.

[0094] Since implied durations may change over time, the annotation
mechanisms may be utilized on a periodic (scheduled) or continuous basis.
Moreover, the annotation mechanisms may operate in response to
predetermined events, e.g., updates to the corpus, user request to
initiate annotation operations, or the like. With regard to event based
operations, the annotation operations may be focused on those portions of
the corpus that have changed since a last execution of the annotation
logic on the corpus, for example, e.g., only operating on the patient
medical records that have changed since a last annotation operation.
Thus, the documents in the corpus are maintained with a most up-to-date
version of the implicit duration annotations as possible to ensure proper
answering of questions by the QA system.

[0095] It should be apparent to those of ordinary skill in the art that
although the illustrative embodiments are described in the context of the
medical domain with operations being performed on patient medical records
and evaluating medical policies, the invention is not limited to such.
Rather, the mechanisms of the illustrative embodiments are applicable to
any domain where date/time based entries are utilized and implicit
durations may be important to decision making, information reporting, or
question answering. Just as one other example, the mechanisms of the
illustrative embodiments may be used with regard to automotive repair
where records are maintained of services performed on an automobile at
various times and decisions may need to be made or questions may need to
be answered regarding services to perform or provide to an owner of the
automobile based on duration based policies. In such a domain, the
automotive repair/service records may have entries with associated
dates/times but may not explicitly indicate durations. As such, the
mechanisms of the illustrative embodiments may be utilized to annotate
such automotive repair/service records with implicit duration annotations
that can then be used to evaluate against automotive repair/service
policies of an automotive insurance company, automobile manufacturer,
governmental organization governing the particular type of automobile or
service offered using the automobile (e.g., the trucking industry), or
the like. Of course other domains where the mechanisms of the
illustrative embodiments may be implemented will become apparent to those
of ordinary skill in the art in view of the present description.

[0096] FIG. 5 is a flowchart outlining an example operation of a QA system
implementing an implicit duration annotation mechanism in accordance with
one illustrative embodiment. As shown in FIG. 5, the operation initially
starts with the ingestion of a corpus of documents (step 510). As part of
the ingestion operation, for each document or subset of related
documents, dates/times associated with, or identified in, the documents
are identified along with the corresponding concepts to generate entries
in a document duration data structure (step 520). Entries in the document
duration data structure are grouped according to related concepts and one
or more implicit durations associated with the group are calculated (step
530). One or more annotations corresponding to the implicit duration are
generated which correlates the one or more implicit durations with
corresponding concepts (step 540). These annotations are inserted into
the documents of the group, or into a primary document of the group (step
550).

[0097] At some point later, a question is received for processing by the
QA system (step 560). The question is processed by the QA system to
generate queries that applied to the annotated corpus (step 570). The
implicit duration annotations of documents found as a result of the
queries, i.e. that provide candidate answers or support for candidate
answers, are compared against one or more duration-based policies of a
policies database (which may itself be part of the corpus) (step 580).
Based on the comparison of the implicit duration annotations with the
duration-based policies, one or more final candidate answers are
generated and returned for output to the submitter of the question (step
590). The operation then terminates.

[0098] As noted above, it should be appreciated that the illustrative
embodiments may take the form of an entirely hardware embodiment, an
entirely software embodiment or an embodiment containing both hardware
and software elements. In one example embodiment, the mechanisms of the
illustrative embodiments are implemented in software or program code,
which includes but is not limited to firmware, resident software,
microcode, etc.

[0099] A data processing system suitable for storing and/or executing
program code will include at least one processor coupled directly or
indirectly to memory elements through a system bus. The memory elements
can include local memory employed during actual execution of the program
code, bulk storage, and cache memories which provide temporary storage of
at least some program code in order to reduce the number of times code
must be retrieved from bulk storage during execution.

[0100] Input/output or I/O devices (including but not limited to
keyboards, displays, pointing devices, etc.) can be coupled to the system
either directly or through intervening I/O controllers. Network adapters
may also be coupled to the system to enable the data processing system to
become coupled to other data processing systems or remote printers or
storage devices through intervening private or public networks. Modems,
cable modems and Ethernet cards are just a few of the currently available
types of network adapters.

[0101] The description of the present invention has been presented for
purposes of illustration and description, and is not intended to be
exhaustive or limited to the invention in the form disclosed. Many
modifications and variations will be apparent to those of ordinary skill
in the art without departing from the scope and spirit of the described
embodiments. The embodiment was chosen and described in order to best
explain the principles of the invention, the practical application, and
to enable others of ordinary skill in the art to understand the invention
for various embodiments with various modifications as are suited to the
particular use contemplated. The terminology used herein was chosen to
best explain the principles of the embodiments, the practical application
or technical improvement over technologies found in the marketplace, or
to enable others of ordinary skill in the art to understand the
embodiments disclosed herein.